Will It Run AI

Can GPT-OSS 20B run on GTX 1080 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

GPT-OSS 20B needs ~17.3 GB but GTX 1080 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.3 GB, exceeds 8.0 GB available
17.3 GB required8.0 GB available
216% VRAM needed

9.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.4 tok/s

TTFT

35610 ms

Safe context

4K

Memory

17.3 GB / 8.0 GB

Offload

50%

Memory breakdown

Weights12.8 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsGPT-OSS 20B on GTX 1080 8GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 5.4 tok/s decode · 35.6s TTFT (warm) · 14 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.3 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy5.7 tok/s18380 ms4K
CodingFToo heavy5.4 tok/s35610 ms4K
Agentic CodingFToo heavy5.4 tok/s51797 ms4K
ReasoningFToo heavy5.4 tok/s42085 ms4K
RAGFToo heavy5.1 tok/s69602 ms4K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
8.2 GB
LowF0
Q3_K_S
3
10.3 GB
LowF0
NVFP4
4
11.8 GB
MediumF0
Q4_K_M
4
12.8 GB
MediumF0
Q5_K_M
5
15.1 GB
HighF0
Q6_K
6
17.2 GB
HighF0
Q8_0
8
22.5 GB
Very HighF0
F16
16
43.1 GB
MaximumF0

升级选项

能流畅运行 GPT-OSS 20B 的硬件

NVIDIARTX 5060 Ti 16GB最佳性价比
16 GB VRAM (+8)448 GB/s (+128)
A
Makes the model fit on the accelerator instead of staying completely out of reach.31.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 491%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBNVIDIA 升级
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.23.5 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 335%.

~$499 MSRP

NVIDIARTX 4000 Ada 20GB经济之选
20 GB VRAM (+12)360 GB/s (+40)
S
Makes the model fit on the accelerator instead of staying completely out of reach.53.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,250 MSRP

Frequently asked questions

Can GTX 1080 8GB run GPT-OSS 20B?

No, GPT-OSS 20B requires more memory than GTX 1080 8GB provides.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 17.3 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 20B?

The recommended quantization for GPT-OSS 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 20B run at on GTX 1080 8GB?

On GTX 1080 8GB, GPT-OSS 20B achieves approximately 5.4 tokens per second decode speed with a time-to-first-token of 35610ms using Q4_K_M quantization.

Can GTX 1080 8GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on GTX 1080 8GB receives a F grade with 5.4 tok/s and 4K context.

What context window can GPT-OSS 20B use on GTX 1080 8GB?

On GTX 1080 8GB, GPT-OSS 20B can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 20B feels slow on GTX 1080 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1080 8GBSee all hardware for GPT-OSS 20B
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